Rheumatoid Arthritis (RA) is an autoimmune disease of unknown aetiology involving complex interactions between environmental and genetic factors. Its pathogenesis is suspected to arise from intricate interplays between signalling, gene regulation and metabolism, leading to synovial inflammation, bone erosion and cartilage destruction in the patients’ joints. In addition, the resident synoviocytes of macrophage and fibroblast types can interact with innate and adaptive immune cells and contribute to the disease’s debilitating symptoms. Therefore, a detailed, mechanistic mapping of the molecular pathways and cellular crosstalks is essential to understand the complex biological processes and different disease manifestations. In this regard, we present the RA-Atlas, an SBGN-standardized, interactive, manually curated representation of existing knowledge related to the onset and progression of RA. This state-of-the-art RA-Atlas includes an updated version of the global RA-map covering relevant metabolic pathways and cell-specific molecular interaction maps for CD4+ Th1 cells, fibroblasts, and M1 and M2 macrophages. The molecular interaction maps were built using information extracted from published literature and pathway databases and enriched using omic data. The RA-Atlas is freely accessible on the webserver MINERVA (https://ramap.uni.lu/minerva/), allowing easy navigation using semantic zoom, cell-specific or experimental data overlay, gene set enrichment analysis, pathway export or drug query.

Fibroblasts, the most abundant cells in the connective tissue, are key modulators of the extracellular matrix (ECM) composition. These spindle-shaped cells are capable of synthesizing various extracellular matrix proteins and collagen. They also provide the structural framework (stroma) for tissues and play a pivotal role in the wound healing process. While they are maintainers of the ECM turnover and regulate several physiological processes, they can also undergo transformations responding to certain stimuli and display aggressive phenotypes that contribute to disease pathophysiology. In this review, we focus on the metabolic pathways of glucose and highlight metabolic reprogramming as a critical event that contributes to the transition of fibroblasts from quiescent to activated and aggressive cells. We also cover the emerging evidence that allows us to draw parallels between fibroblasts in autoimmune disorders and more specifically in rheumatoid arthritis and cancer. We link the metabolic changes of fibroblasts to the toxic environment created by the disease condition and discuss how targeting of metabolic reprogramming could be employed in the treatment of such diseases. Lastly, we discuss Systems Biology approaches, and more specifically, computational modeling, as a means to elucidate pathogenetic mechanisms and accelerate the identification of novel therapeutic targets.

Rheumatoid Arthritis (RA) is an autoimmune disease characterized by a highly invasive pannus formation consisting mainly of Synovial Fibroblasts (RASFs). This pannus leads to cartilage, bone, and soft tissue destruction in the affected joint. RASFs’ activation is associated with metabolic alterations resulting from dysregulation of extracellular signals’ transduction and gene regulation. Deciphering the intricate mechanisms at the origin of this metabolic reprogramming may provide sign insight into RASFs’ involvement in RA’s pathogenesis and offer new therapeutic strategies. Qualitative and quantitative dynamic modeling can address some of these features, but hybrid models represent a real asset in their ability to span multiple layers of biological machinery. This work presents the first hybrid RASF model: the combination of a cell-specific qualitative regulatory network with a global metabolic network. The automated framework for hybrid modeling exploits the regulatory network’s trap-spaces as additional constraints on the metabolic network. Subsequent flux balance analysis allows assessment of RASFs’ regulatory outcomes’ impact on their metabolic flux distribution. The hybrid RASF model reproduces the experimentally observed metabolic reprogramming induced by signaling and gene regulation in RASFs. Simulations also enable further hypotheses on the potential reverse Warburg effect in RA. RASFs may undergo metabolic reprogramming to turn into "metabolic factories", producing high levels of energy-rich fuels and nutrients for neighboring demanding cells through the crucial role of HIF1.

Fibroblasts, the most abundant cells in the connective tissue, are key modulators of the extracellular matrix (ECM) composition. These spindle-shaped cells are capable of synthesizing various extracellular matrix proteins and collagen. They also provide the structural framework (stroma) for tissues and play a pivotal role in the wound healing process. While they are maintainers of the ECM turnover and regulate several physiological processes, they can also undergo transformations responding to certain stimuli and display aggressive phenotypes that contribute to disease pathophysiology. In this review, we focus on the metabolic pathways of glucose and highlight metabolic reprogramming as a critical event that contributes to the transition of fibroblasts from quiescent to activated and aggressive cells. We also cover the emerging evidence that allows us to draw parallels between fibroblasts in autoimmune disorders and more specifically in rheumatoid arthritis and cancer. We link the metabolic changes of fibroblasts to the toxic environment created by the disease condition and discuss how targeting of metabolic reprogramming could be employed in the treatment of such diseases. Lastly, we discuss Systems Biology approaches, and more specifically, computational modelling, as a means to elucidate pathogenetic mechanisms and accelerate the identification of novel therapeutic targets.

Rheumatoid Arthritis (RA) is an autoimmune disease characterized by a highly invasive pannus formation consisting mainly of synovial fibroblasts (RASFs). This pannus leads to cartilage, bone and soft tissue destruction in the affected joint. RASFs' activation is associated with metabolic alterations resulting from dysregulation of extracellular signals transduction and gene regulation machinery. Deciphering the intricate mechanisms at the origin of this metabolic reprogramming may provide significant insight into RASFs' involvement in RA's pathogenesis and offer new therapeutic strategies. Qualitative and quantitative dynamic modeling can address some of these features, but hybrid models represent a real asset in their ability to span multiple layers of biological machinery. This work presents the first hybrid RASF model: the combination of a cellspecific qualitative regulatory network with a global metabolic network. The automated framework for hybrid modeling exploits the regulatory network's trap-spaces as additional constraints on the metabolic networks. Subsequent flux balance analysis allows assessment of RASFs' regulatory outcomes' impact on their metabolic flux distribution. The hybrid RASF model simulates the experimentally observed metabolic reprogramming induced by signaling and gene regulation in RASFs. Simulations also enable further hypotheses on the potential reverse Warburg effect in RA. RASFs may undergo metabolic reprogramming to turn into "metabolic factories", producing high levels of energy-rich fuels and nutrients for neighboring demanding cells through the crucial role of HIF1.